### 机器学习代写|tensorflow代写|Understanding code as a graph

TensorFlow是一个用于机器学习和人工智能的免费和开源的软件库。它可以用于一系列的任务，但特别关注深度神经网络的训练和推理。

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 机器学习代写|tensorflow代写|Understanding code as a graph

Consider a doctor who predicts the expected weight of a newborn to be $7.5$ pounds. You’d like to figure out how that prediction differs from the actual measured weight. Being an overly analytical engineer, you design a function to describe the likelihood of all possible weights of the newborn. A weight of 8 pounds is more likely than 10 pounds, for example.

You can choose to use the Gaussian (otherwise known as normal) probability distribution function. This function takes a number as input and outputs a non-negative number describing the probability of observing the input. This function shows up all the time in machine learning and is easy to define in TensorFlow. It uses multiplication, division, negation, and a couple of other fundamental operators.

Think of every operator as being a node in a graph. Whenever you see a plus symbol (+) or any mathematical concept, picture it as one of many nodes. The edges between these nodes represent the composition of mathematical functions. Specifically, the negative operator we’ve been studying is a node, and the incoming/outgoing edges of this node are how the Tensor transforms. A tensor flows through the graph, which is why this library is called TensorFlow.

Here’s a thought: every operator is a strongly typed function that takes input tensors of a dimension and produces output of the same dimension. Figure $2.3$ is an example of how the Gaussian function can be designed with TensorFlow. The function is represented as a graph in which operators are nodes and edges represent interactions between nodes. This graph as a whole represents a complicated mathematical function (specifically, the Gaussian function). Small segments of the graph represent simple mathematical concepts, such as negation and doubling.

TensorFlow algorithms are easy to visualize. They can be described simply by flowcharts. The technical (and more correct) term for such a flowchart is a dataflow graph. Every arrow in a dataflow graph is called an edge. In addition, every state of the dataflow graph is called a node. The purpose of the session is to interpret your Python code into a dataflow graph and then associate the computation of each node of the graph to the CPU or GPU.

## 机器学习代写|tensorflow代写|Setting session configurations

You can also pass options to tf.Session. TensorFlow automatically determines the best way to assign a GPU or CPU device to an operation, for example, depending on what’s available. You can pass an additional option, log_device_placement=True, when creating a session. Listing $2.7$ shows you exactly where on your hardware the computations are evoked.

import tensorflow as tf
options = tf.RunOptions (output partition_graphs=True)
result = sess . run (negMatrix, options=options, run_metadata=metadata) \& _ Prints the resulting value
This code outputs info about which CPU/GPU devices are used in the session for each operation. Running listing $2.7$ results in traces of output like the following to show which device was used to run the negation op:
Sessions are essential in TensorFlow code. You need to call a session to “run” the math. Figure $2.4$ maps out how the components on TensorFlow interact with the machinelearning pipeline. A session not only runs a graph operation, but also can take placeholders, variables, and constants as input. We’ve used constants so far, but in later sections, we’ll start using variables and placeholders. Here’s a quick overview of these three types of values:

• Placeholder-A value that’s unassigned but will be initialized by the session wherever it’s run. Typically, placeholders are the input and output of your model.
• Variable-A value that can change, such as parameters of a machine-learning model. Variables must be initialized by the session before they’re used.
• Constant-A value that doesn’t change, such as a hyperparameter or setting.
The entire pipeline for machine learning with TensorFlow follows the flow of figure 2.4. Most of the code in TensorFlow consists of setting up the graph and session. After you design a graph and hook up the session to execute it, your code is ready to use.

## 机器学习代写|tensorflow代写|Writing code in Jupyter

Because TensorFlow is primarily a Python library, you should make full use of Python’s interpreter. Jupyter is a mature environment for exercising the interactive nature of the language. It’s a web application that displays computation elegantly so that you can share annotated interactive algorithms with others to teach a technique or demonstrate code. Jupyter also easily integrates with visualization libraries like Python’s Matplotlib and can be used to share elegant data stories about your algorithm, to evaluate its accuracy, and to present results.

You can share your Jupyter notebooks with other people to exchange ideas, and you can download their notebooks to learn about their code. See the appendix to get started installing the Jupyter Notebook application.

From a new terminal, change the directory to the location where you want to practice TensorFlow code, and start a notebook server:
\$cd -/MyTensorFlowStuff$\ jupyter notebook
Running this command should launch a new browser window with the Jupyter notebook’s dashboard. If no window opens automatically, you can navigate to http://localhost:8888 from any browser. You’ll see a web page similar to the one in figure $2.5$.
TIP The jupyter notebook command didn’t work? Make sure that your PYTHONPATH environment variable includes the path to the jupyter script created when you installed the library. Also, this book uses both Python $3.7$ (recommended) and Python $2.7$ examples (due to the BregmanToolkit, which you’ll read about in chapter 7). For this reason, you will want to install Jupyter with Python kernels enabled. For more information, see https://ipython readthedocs.io/en/stable/install/kernel_install.html.

## 机器学习代写|tensorflow代写|Understanding code as a graph

TensorFlow 算法易于可视化。它们可以简单地用流程图来描述。这种流程图的技术（更正确）术语是数据流图。数据流图中的每个箭头都称为边。此外，数据流图的每个状态都称为一个节点。会话的目的是将您的 Python 代码解释为数据流图，然后将图的每个节点的计算与 CPU 或 GPU 相关联。

## 机器学习代写|tensorflow代写|Setting session configurations

options = tf.RunOptions (output partition_graphs=True)

• 占位符 – 一个未分配的值，但无论在何处运行，会话都会对其进行初始化。通常，占位符是模型的输入和输出。
• 变量 – 可以更改的值，例如机器学习模型的参数。变量在使用之前必须由会话初始化。
• 常量 – 不会改变的值，例如超参数或设置。
使用 TensorFlow 进行机器学习的整个管道遵循图 2.4 的流程。TensorFlow 中的大部分代码都包括设置图和会话。设计图表并连接会话以执行它之后，您的代码就可以使用了。

## 机器学习代写|tensorflow代写|Writing code in Jupyter

$cd -/MyTensorFlowStuff$jupyter notebook

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## MATLAB代写

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